{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial 11: DeepWalk and node2vec - Implementation details\n",
    "  \n",
    "\n",
    "Paper:\n",
    "* [DeepWalk: Online Learning of Social Representation](https://arxiv.org/pdf/1403.6652.pdf)  \n",
    "* [node2vec: Scalable Feature Learning for Networks](https://arxiv.org/pdf/1607.00653.pdf)  \n",
    "\n",
    "Code:\n",
    "\n",
    " * [node2vec doc](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html?highlight=node2vec#torch_geometric.nn.models.Node2Vec)\n",
    " * [node2vec code](https://pytorch-geometric.readthedocs.io/en/latest/_modules/torch_geometric/nn/models/node2vec.html)\n",
    " * [Example on clustering](https://github.com/rusty1s/pytorch_geometric/blob/master/examples/node2vec.py)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch_geometric.nn import Node2Vec\n",
    "import os.path as osp\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.manifold import TSNE\n",
    "from torch_geometric.datasets import Planetoid\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = 'Cora'\n",
    "path = osp.join('.', 'data', dataset)\n",
    "dataset = Planetoid(path, dataset)\n",
    "data = dataset[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "model = Node2Vec(data.edge_index, embedding_dim=128, \n",
    "                 walk_length=20,                        # lenght of rw\n",
    "                 context_size=10, walks_per_node=20,\n",
    "                 num_negative_samples=1, \n",
    "                 p=200, q=1,                             # bias parameters\n",
    "                 sparse=True).to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random walks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The data loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = model.loader(batch_size=128, shuffle=True, num_workers=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 torch.Size([28160, 10]) torch.Size([28160, 10])\n",
      "1 torch.Size([28160, 10]) torch.Size([28160, 10])\n",
      "2 torch.Size([28160, 10]) torch.Size([28160, 10])\n",
      "3 torch.Size([28160, 10]) torch.Size([28160, 10])\n",
      "4 torch.Size([28160, 10]) torch.Size([28160, 10])\n",
      "5 torch.Size([28160, 10]) torch.Size([28160, 10])\n",
      "6 torch.Size([28160, 10]) torch.Size([28160, 10])\n",
      "7 torch.Size([28160, 10]) torch.Size([28160, 10])\n",
      "8 torch.Size([28160, 10]) torch.Size([28160, 10])\n",
      "9 torch.Size([28160, 10]) torch.Size([28160, 10])\n",
      "10 torch.Size([28160, 10]) torch.Size([28160, 10])\n",
      "11 torch.Size([28160, 10]) torch.Size([28160, 10])\n",
      "12 torch.Size([28160, 10]) torch.Size([28160, 10])\n",
      "13 torch.Size([28160, 10]) torch.Size([28160, 10])\n",
      "14 torch.Size([28160, 10]) torch.Size([28160, 10])\n",
      "15 torch.Size([28160, 10]) torch.Size([28160, 10])\n",
      "16 torch.Size([28160, 10]) torch.Size([28160, 10])\n",
      "17 torch.Size([28160, 10]) torch.Size([28160, 10])\n",
      "18 torch.Size([28160, 10]) torch.Size([28160, 10])\n",
      "19 torch.Size([28160, 10]) torch.Size([28160, 10])\n",
      "20 torch.Size([28160, 10]) torch.Size([28160, 10])\n",
      "21 torch.Size([4400, 10]) torch.Size([4400, 10])\n"
     ]
    }
   ],
   "source": [
    "for idx, (pos_rw, neg_rw) in enumerate(loader):\n",
    "    print(idx, pos_rw.shape, neg_rw.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx, (pos_rw, neg_rw) = next(enumerate(loader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([28160, 10]), torch.Size([28160, 10]))"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pos_rw.shape, neg_rw.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[2614, 1599, 2441,  ...,    9,  723,    9],\n",
       "        [ 905,  389,  359,  ..., 1427,  860, 1758],\n",
       "        [ 711, 2112,  711,  ...,  596, 1914,  778],\n",
       "        ...,\n",
       "        [2692, 1310, 2692,  ..., 1310, 2692, 1310],\n",
       "        [1325, 2063, 1326,  ..., 1996, 2064, 1996],\n",
       "        [ 838,  868, 1341,  ..., 2154, 1291, 1224]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_rw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[2614, 1440, 2576,  ...,   28, 1814, 1917],\n",
       "        [ 905,  413, 1185,  ..., 1965,  560, 1576],\n",
       "        [ 711,  513,  727,  ..., 2031, 2118, 2195],\n",
       "        ...,\n",
       "        [1710, 1693,  996,  ..., 1659, 1977,  414],\n",
       "        [ 120, 2312, 1755,  ..., 1517, 1321, 1006],\n",
       "        [2317,  189, 2274,  ..., 2189,  496, 1084]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neg_rw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx \n",
    "edge_tuples = [tuple(x) for x in data.edge_index.numpy().transpose()]\n",
    "G = nx.from_edgelist(edge_tuples)\n",
    "pos = nx.spring_layout(G, center=[0.5, 0.5])\n",
    "nx.set_node_attributes(G, pos, 'pos')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nodelist = next(enumerate(loader))[1][0][0].tolist()\n",
    "walk = nx.path_graph(len(nodelist))\n",
    "nx.set_node_attributes(walk, {idx: pos[node_id] for idx, node_id in enumerate(nodelist)}, 'pos')\n",
    "\n",
    "fig = plt.figure(figsize=(20, 10))\n",
    "ax = fig.add_subplot(1, 2, 1)\n",
    "nx.draw_networkx_nodes(G, \n",
    "                       ax=ax,\n",
    "                       pos=nx.get_node_attributes(G, 'pos'), \n",
    "                       node_size=5,\n",
    "                       alpha=0.3,\n",
    "                       node_color='b')\n",
    "nx.draw(walk, \n",
    "        node_size=40,\n",
    "        node_color='r',\n",
    "        ax=ax,\n",
    "        pos=nx.get_node_attributes(walk, 'pos'), \n",
    "        width=2,\n",
    "        edge_color='r') \n",
    "ax = fig.add_subplot(1, 2, 2)\n",
    "nx.draw(walk, \n",
    "        node_size=40,\n",
    "        node_color='r',\n",
    "        ax=ax,\n",
    "        pos=nx.get_node_attributes(walk, 'pos'), \n",
    "        width=2,\n",
    "        edge_color='r') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Node2Vec(data.edge_index, embedding_dim=128, walk_length=20,\n",
    "                 context_size=10, walks_per_node=10,\n",
    "                 num_negative_samples=1, p=1, q=1, sparse=True).to(device)\n",
    "\n",
    "loader = model.loader(batch_size=128, shuffle=True, num_workers=4)\n",
    "optimizer = torch.optim.SparseAdam(list(model.parameters()), lr=0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train():\n",
    "    model.train()\n",
    "    total_loss = 0\n",
    "    for pos_rw, neg_rw in tqdm(loader):\n",
    "        optimizer.zero_grad()\n",
    "        loss = model.loss(pos_rw.to(device), neg_rw.to(device))\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        total_loss += loss.item()\n",
    "    return total_loss / len(loader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data(edge_index=[2, 10556], test_mask=[2708], train_mask=[2708], val_mask=[2708], x=[2708, 1433], y=[2708])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([3, 4, 4,  ..., 3, 3, 3])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0, 1, 2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.y.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "@torch.no_grad()\n",
    "def test():\n",
    "    model.eval()\n",
    "    z = model()\n",
    "    acc = model.test(z[data.train_mask], data.y[data.train_mask],\n",
    "                     z[data.test_mask], data.y[data.test_mask],\n",
    "                     max_iter=150)\n",
    "    return acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
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      "\n",
      "Epoch: 01, Loss: 0.8250, Acc: 0.7120\n"
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     "text": [
      "\n",
      "Epoch: 02, Loss: 0.8263, Acc: 0.7120\n"
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     "text": [
      "\n",
      "Epoch: 03, Loss: 0.8256, Acc: 0.7080\n"
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    {
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     "text": [
      "\n",
      "Epoch: 04, Loss: 0.8254, Acc: 0.6990\n"
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     "text": [
      "\n",
      "Epoch: 05, Loss: 0.8247, Acc: 0.6910\n"
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    },
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     "text": [
      "\n",
      "Epoch: 06, Loss: 0.8243, Acc: 0.6990\n"
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     "text": [
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      "Epoch: 07, Loss: 0.8252, Acc: 0.7070\n"
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      "Epoch: 08, Loss: 0.8256, Acc: 0.7150\n"
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      "\n",
      "Epoch: 09, Loss: 0.8243, Acc: 0.7070\n"
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     "text": [
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      "Epoch: 10, Loss: 0.8252, Acc: 0.7030\n"
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     "text": [
      "\n",
      "Epoch: 11, Loss: 0.8239, Acc: 0.7070\n"
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     "text": [
      "\n",
      "Epoch: 12, Loss: 0.8250, Acc: 0.6990\n"
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      "Epoch: 13, Loss: 0.8249, Acc: 0.6970\n"
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      "Epoch: 14, Loss: 0.8255, Acc: 0.6990\n"
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      "Epoch: 16, Loss: 0.8250, Acc: 0.6990\n"
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      "Epoch: 17, Loss: 0.8249, Acc: 0.7070\n"
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      "Epoch: 18, Loss: 0.8255, Acc: 0.7100\n"
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      "Epoch: 19, Loss: 0.8242, Acc: 0.6950\n"
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      "Epoch: 20, Loss: 0.8246, Acc: 0.6960\n"
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      "Epoch: 24, Loss: 0.8252, Acc: 0.7180\n"
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      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=22.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 182, Loss: 0.8255, Acc: 0.7030\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7eb304a437d14dae8dfae37338e6e0c2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=22.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 183, Loss: 0.8246, Acc: 0.7150\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "095fcd74b816417ba7de2f669e900680",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=22.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 184, Loss: 0.8250, Acc: 0.7080\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3b8526a1d1314840a6fec35a55d88c37",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=22.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 185, Loss: 0.8247, Acc: 0.7020\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0067ac8c21be41c29be9123d9cbd393d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=22.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 186, Loss: 0.8250, Acc: 0.7130\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e9d92335927747d0bf4476d5ef59ac58",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=22.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 187, Loss: 0.8256, Acc: 0.7170\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a9a8c7c5912d4322bca6cc4c78eac869",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=22.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 188, Loss: 0.8250, Acc: 0.7140\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "81a64f74fa2c4e4ab53d639ea13119df",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=22.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 189, Loss: 0.8241, Acc: 0.7110\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b9770f96ff854924be1b9ae05f15c38e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=22.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 190, Loss: 0.8265, Acc: 0.7010\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "20eab350a2c3437f9165459852e8667b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=22.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 191, Loss: 0.8244, Acc: 0.7100\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "afff01a9cc324c839e10756325b473b2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=22.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 192, Loss: 0.8248, Acc: 0.7030\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2d356a50459c40ba8577182c7faa209a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=22.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 193, Loss: 0.8257, Acc: 0.6940\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "36d56c1711fd40f285b550944ddf4a30",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=22.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 194, Loss: 0.8245, Acc: 0.6970\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "797f954f13b1473e9be7deb785fc354a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=22.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 195, Loss: 0.8248, Acc: 0.7070\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b58933a049b54cb596e777782d90c4a3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=22.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 196, Loss: 0.8247, Acc: 0.7120\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e1ce2bbcbe6b48f69ebcd17c951b1272",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=22.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 197, Loss: 0.8250, Acc: 0.7190\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "31028a02b1ee43728d576ed9f164544b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=22.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 198, Loss: 0.8257, Acc: 0.7100\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "97e33e7399bf485a9d021f46fcf36a54",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=22.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 199, Loss: 0.8252, Acc: 0.7090\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fee91ff6a0014f99b40d2584f9c85602",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=22.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 200, Loss: 0.8241, Acc: 0.7200\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(1, 201):\n",
    "    loss = train()\n",
    "    acc = test()\n",
    "    print(f'Epoch: {epoch:02d}, Loss: {loss:.4f}, Acc: {acc:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "@torch.no_grad()\n",
    "def plot_points(colors):\n",
    "    model.eval()\n",
    "    z = model(torch.arange(data.num_nodes, device=device))\n",
    "    z = TSNE(n_components=2).fit_transform(z.cpu().numpy())\n",
    "    y = data.y.cpu().numpy()\n",
    "\n",
    "    plt.figure(figsize=(8, 8))\n",
    "    for i in range(dataset.num_classes):\n",
    "        plt.scatter(z[y == i, 0], z[y == i, 1], s=20, color=colors[i])\n",
    "    plt.axis('off')\n",
    "    plt.show()\n",
    "\n",
    "colors = [\n",
    "    '#ffc0cb', '#bada55', '#008080', '#420420', '#7fe5f0', '#065535',\n",
    "    '#ffd700'\n",
    "]\n",
    "plot_points(colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
